S-Cube Learning PackageA Soft-Constraint Based Approach to QoS-Aware Service                       Selection            Un...
Learning Package Categorization                        S-Cube                  Quality Definition,               Negotiati...
Service          Selection             and       QoS Service selection is the first step to improve service composition w...
Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
Problem Description: Service Selection Scenario                                                             Select only on...
Problem Description:Service Selection Techniques in the Literature 1  Constraint Satisfaction Problem (CSP):   •  Classic...
Problem Description:Service Selection Techniques in the Literature 1 Soft Constraint Satisfaction Problem (SCSP)   •  Inc...
Problem Description:  Service Selection Techniques in the Literature 1                                                    ...
Problem Description: Problem at Design-time      2.  I have to fix         new criteria                             1.  Re...
Problem Description:Problem at Runtime   !        Some problems, encountered by the service may        lead to service mal...
Problem Description:SLASLA - Definition:  “An XML document and a contract for…         •  Advertising the quality level of...
Problem Description: 2Problem at Runtime    Where are My preferencesand the penalties?                         Out of     ...
Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
Main ObjectiveAutomatically switch from a faultyservice to a new one           User request (preferences,                 ...
Approach Main Points Definition of Soft Service Level Agreement (SSLA) an SLA model extended with preferences and penaltie...
Kinds of penalties Arithmetical Penalties   •  In relation with measurable qualities of service   •  Direct relation to s...
Soft SLA Definition
Soft SLA Definition:Preferences & Penalties     I prefer to get a payment   service and delivery service  having response ...
Soft SLA Definition Guarantee terms are expressed in terms of preferences and penalties   •  Preferences are ranked (most...
Extending SCSP Using Penalties              SCSP                     Constraint                      System               ...
Extending Constraint System SCSP        Constraint                                  CS = <S; D{}; V>         System       ...
Extending Constraints Using Penalties SCSP        Constraint                               Def = Definition of the        ...
Rewrite operations Logic SCSP        Constraint         System              Combination       =                           ...
Extending SCSP Using Penalties SCSP        Constraint         System                                    Global Preferences...
Penalty based SCSPCase Study Penalty based SCSP        Constraint         System       Constraints    = Penalty values    ...
Penalty based SCSPCase Study Penalty based SCSP        Constraint         System       Constraints       Operations       ...
Penalty based SCSPCase Study Penalty based SCSP        Constraint         System       Constraints       Operations       ...
Penalty based SCSPCase Study Penalty based SCSP        Constraint         System       Constraints       Operations       ...
Proposed Approach LogicInput: Constraints, penalties, table of constraint definitionsOutput: Choices with their possible a...
Mapping SSLA onto SCSP Solvers
Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
Conclusions1.  Soft constraint-based framework2.  Express QoS properties reflecting both customer    preferences and penalt...
References This presentation is based on [ZBC10]
Further S-Cube Reading[ZBC10]      Mohamed Anis    Zemni,    Salima   Benbernou,   and            Manuel Carro            ...
Acknowledgements The research leading to these results has received funding from:   The European Community’s Seventh Fram...
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S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection

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S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection

  1. 1. S-Cube Learning PackageA Soft-Constraint Based Approach to QoS-Aware Service Selection Université Paris-DESCARTES Mohamed-Anis ZEMNI, Salima BENBERNOU, Manuel CARRO www.s-cube-network.eu
  2. 2. Learning Package Categorization S-Cube Quality Definition, Negotiation and Assurance Quality Management and Prediction Analysis Operations on SLAs: Detecting and Explaining Conflicting SLAs
  3. 3. Service Selection and QoS Service selection is the first step to improve service composition within Service-Oriented-Architecture (SOA): •  Searches for services fitting users’ requirements •  Explores services’ properties •  Aims at putting together several elementary services •  Generates new value-added service Quality of Service (QoS) for selection often critically important: •  Software services expose not only functional characteristics, but also non-functional attributes describing their QoS •  Defines the service level (Key Performance Indicator) •  A service fulfilling all the functionality but with low QoS is not interesting
  4. 4. Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
  5. 5. Problem Description: Service Selection Scenario Select only one service among the available services that have the same functionalities but with different QoS Functionalities + QoSUser request (criteria) 1 2 Used Approach at Design-time
  6. 6. Problem Description:Service Selection Techniques in the Literature 1  Constraint Satisfaction Problem (CSP): •  Classical formulation of constraints •  Quite expressive to represent several real life problems •  Defines a set of variables, each of them ranging on a finite domain, and a set of constraints restricting the values that these variables can take simultaneously •  All the constraints must be satisfied simultaneously ! Lack of built-in capabilities to express preferences among constraints and the lack of possibility of giving approximate solutions for problems which are overconstrained
  7. 7. Problem Description:Service Selection Techniques in the Literature 1 Soft Constraint Satisfaction Problem (SCSP) •  Include the concept of preferences into every constraint in order to obtain a suitable solution which can be optimal or, in general, a reasonable estimation, maybe at the expense of not fulfilling all constraints •  Relies on composing the constraints in order to obtain the optimal solution •  Applied to the requirements (in terms of preferences) of the users ! Only one solution returned that is optimal * Stefano Bistarelli, Ugo Montanari, and Francesca Rossi. Semiring- based constraint satisfaction and optimization. J. ACM, 44(2):201– 236, 1997
  8. 8. Problem Description: Service Selection Techniques in the Literature 1 C-semi-ring : Algebraic structure Only one domain for all variablesExample : Searching for services Available at y% of the time and with reputation = z
  9. 9. Problem Description: Problem at Design-time 2.  I have to fix new criteria 1.  Required criteria cannot match any service!!!User request (criteria)
  10. 10. Problem Description:Problem at Runtime ! Some problems, encountered by the service may lead to service malfunctions activity interrupted, must apply penalty!!! Out of service Out of service contract violation
  11. 11. Problem Description:SLASLA - Definition: “An XML document and a contract for… •  Advertising the quality level of the services •  Taking note about the user preferences •  …” I want an SLA ensuring the performances I am searching for Propertie s Pro perties QoS ?
  12. 12. Problem Description: 2Problem at Runtime Where are My preferencesand the penalties? Out of service Out of service
  13. 13. Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
  14. 14. Main ObjectiveAutomatically switch from a faultyservice to a new one User request (preferences, … Out of service Out of penalties) service Design-time Runtime
  15. 15. Approach Main Points Definition of Soft Service Level Agreement (SSLA) an SLA model extended with preferences and penalties Extension of Soft Constraint Solving Problem handling penalties: Define in SSLA the penalty artifacts, such that, if a selected service failed, another one should replace it that fitting with the agreed QoS in the contract with penalties if some of them are not fulfilled SSLA to SCSP mapping
  16. 16. Kinds of penalties Arithmetical Penalties •  In relation with measurable qualities of service •  Direct relation to service variables •  E.g. availability, the response time, the reputation, etc. •  The application of arithmetical penalties is a consequence of a contract breach and therefore the transition to a different selection using the choices expressed by the customer in the form of preferences Behavioural Penalties •  Related to the behavior of either the customer or the service provider •  The application of behavioral penalties is not always a consequence of a contract breach and so, switching to another choice is not obligatory and even less replacing the service
  17. 17. Soft SLA Definition
  18. 18. Soft SLA Definition:Preferences & Penalties I prefer to get a payment service and delivery service having response time < 5ms. I also accept services with response time between 5ms and 20ms with preference =0,5 Etc. Response time Preferences If the first Most preferred preference is not <5ms fulfilled during the execution I would apply penalty P7 [5ms,20ms[ >20ms Less preferred
  19. 19. Soft SLA Definition Guarantee terms are expressed in terms of preferences and penalties •  Preferences are ranked (most preferred to less preferred) •  Penalties are applied if a preference is not fulfilled The service broker search for service fulfilling the QoS from the most preferred to the less preferred (at design-time) Penalties are applied only at runtime and never at design- time, on the faulty service SSLA document QoS Variable Preference Preferences Penalties Preferences/Penalties variables doamins degree association
  20. 20. Extending SCSP Using Penalties SCSP Constraint System Constraints Operations Solution
  21. 21. Extending Constraint System SCSP Constraint CS = <S; D{}; V> System S = algebraic structure including preference Constraints values V = QoS variables D{} = Variable domains Operations Penalties into S Solution
  22. 22. Extending Constraints Using Penalties SCSP Constraint Def = Definition of the System constraint in terms of preference value Constraints Type = in terms of variable intervening in the constraint Operations Penalties into Def Solution
  23. 23. Rewrite operations Logic SCSP Constraint System Combination = combination of the constraints (pref) Constraints Projection = generates the optimal solution Operations Rank generated solutions and keep them all Combination of penalties Solution
  24. 24. Extending SCSP Using Penalties SCSP Constraint System Global Preferences Constraints Most preferred + Operations Less preferred - Solution
  25. 25. Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints = Penalty values = Preference values Operations Solutions
  26. 26. Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints Operations Solutions
  27. 27. Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints Operations Solutions
  28. 28. Penalty based SCSPCase Study Penalty based SCSP Constraint System Constraints Operations Solutions
  29. 29. Proposed Approach LogicInput: Constraints, penalties, table of constraint definitionsOutput: Choices with their possible alternatives orderedBegin For each selection alternative do Combine all the constraints together (apply the min operator); End for; Order the results according to preference values into groups; For each preference value group do Order the elements corresponding to the penalty value; End for;End;
  30. 30. Mapping SSLA onto SCSP Solvers
  31. 31. Learning Package Overview  Problem Description  Extending SCSP with Penalties & new SLA Model  Conclusions
  32. 32. Conclusions1.  Soft constraint-based framework2.  Express QoS properties reflecting both customer preferences and penalties applied to unfitting situations3.  Solution for overconstrained problems –  The application of soft constraints makes it possible to work around overconstrained problems and offer a feasible solution4.  Provide ranked choice to offer more flexibility at design-time to find required services, and at runtime to ensure users’ rights5.  Concept of penalties in SCSP We plan to extend this framework to also deal with behavioral penalties
  33. 33. References This presentation is based on [ZBC10]
  34. 34. Further S-Cube Reading[ZBC10] Mohamed Anis Zemni, Salima Benbernou, and Manuel Carro A Soft Constraint-Based Approach to QoS-Aware Service Selection In proceeding of the Service-Oriented Computing - 8th International Conference (ICSOC 2010), volume 6470 of Lecture Notes in Computer Science, pages 596-602 San Francisco, CA, USA, December 7-10, 2010
  35. 35. Acknowledgements The research leading to these results has received funding from:   The European Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement 215483 (S-Cube).
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